Association of multiple genetic variants with chronic obstructive pulmonary disease susceptibility in Hainan region

Genetic Polymorphisms of CYP2C9/CYP2C19 in Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is a high incidence in the elderly and significantly affects the quality of life. CYP2C9 and CYP2C19 play an important role in tobacco-related diseases and inflammatory reactions. Thus, we aim to investigate the association between CYP2C9/CYP2C19 polymorphisms and the risk of COPD. In this study, a total of 821 subjects were recruited which include 313 COPD cases and 508 healthy controls. Seven SNPs of CYP2C9/CYP2C19 were selected for genotyping. The odds ratios (ORs) and 95% confidence interval (95% CI) were calculated using logistic regression analysis to evaluate the association between COPD risk and CYP2C9/CYP2C19 polymorphisms. Our study showed that A allele of rs9332220 in CYP2C9 was associated with reducing COPD risk (OR = 0.64, 95% CI = 0.43-0.94, p = 0.021). And rs111853758 G allele carrier could significantly decrease 0.35-fold COPD risk compared with T allele carrier (OR = 0.65, 95% CI = 0.45-0.96, p = 0.027). Furthermore, sex-based stratification analysis showed that rs9332220 and rs111853758 polymorphisms were associated with the risk of COPD in males. This is the first study to investigate the association between CYP2C9 and CYP2C19 genetic polymorphisms and COPD risk, which may give a new perspective on the prevention and diagnosis of COPD.

Comparison and development of machine learning tools for the prediction of chronic obstructive pulmonary disease in the Chinese population

Background

Chronic obstructive pulmonary disease (COPD) is a major public health problem and cause of mortality worldwide. However, COPD in the early stage is usually not recognized and diagnosed. It is necessary to establish a risk model to predict COPD development.

Methods

A total of 441 COPD patients and 192 control subjects were recruited, and 101 single-nucleotide polymorphisms (SNPs) were determined using the MassArray assay. With 5 clinical features as well as SNPs, 6 predictive models were established and evaluated in the training set and test set by the confusion matrix AU-ROC, AU-PRC, sensitivity (recall), specificity, accuracy, F1 score, MCC, PPV (precision) and NPV. The selected features were ranked.

Results

Nine SNPs were significantly associated with COPD. Among them, 6 SNPs (rs1007052, OR = 1.671, P = 0.010; rs2910164, OR = 1.416, P < 0.037; rs473892, OR = 1.473, P < 0.044; rs161976, OR = 1.594, P < 0.044; rs159497, OR = 1.445, P < 0.045; and rs9296092, OR = 1.832, P < 0.045) were risk factors for COPD, while 3 SNPs (rs8192288, OR = 0.593, P < 0.015; rs20541, OR = 0.669, P < 0.018; and rs12922394, OR = 0.651, P < 0.022) were protective factors for COPD development. In the training set, KNN, LR, SVM, DT and XGboost obtained AU-ROC values above 0.82 and AU-PRC values above 0.92. Among these models, XGboost obtained the highest AU-ROC (0.94), AU-PRC (0.97), accuracy (0.91), precision (0.95), F1 score (0.94), MCC (0.77) and specificity (0.85), while MLP obtained the highest sensitivity (recall) (0.99) and NPV (0.87). In the validation set, KNN, LR and XGboost obtained AU-ROC and AU-PRC values above 0.80 and 0.85, respectively. KNN had the highest precision (0.82), both KNN and LR obtained the same highest accuracy (0.81), and KNN and LR had the same highest F1 score (0.86). Both DT and MLP obtained sensitivity (recall) and NPV values above 0.94 and 0.84, respectively. In the feature importance analyses, we identified that AQCI, age, and BMI had the greatest impact on the predictive abilities of the models, while SNPs, sex and smoking were less important.

Conclusions

The KNN, LR and XGboost models showed excellent overall predictive power, and the use of machine learning tools combining both clinical and SNP features was suitable for predicting the risk of COPD development.

Pathobiological mechanisms underlying metabolic syndrome (MetS) in chronic obstructive pulmonary disease (COPD): clinical significance and therapeutic strategies

Chronic obstructive pulmonary disease (COPD) is a major incurable global health burden and is currently the 4th largest cause of death in the world. Importantly, much of the disease burden and health care utilisation in COPD is associated with the management of its comorbidities (e.g. skeletal muscle wasting, ischemic heart disease, cognitive dysfunction) and infective viral and bacterial acute exacerbations (AECOPD). Current pharmacological treatments for COPD are relatively ineffective and the development of effective therapies has been severely hampered by the lack of understanding of the mechanisms and mediators underlying COPD. Since comorbidities have a tremendous impact on the prognosis and severity of COPD, the 2015 American Thoracic Society/European Respiratory Society (ATS/ERS) Research Statement on COPD urgently called for studies to elucidate the pathobiological mechanisms linking COPD to its comorbidities. It is now emerging that up to 50% of COPD patients have metabolic syndrome (MetS) as a comorbidity. It is currently not clear whether metabolic syndrome is an independent co-existing condition or a direct consequence of the progressive lung pathology in COPD patients. As MetS has important clinical implications on COPD outcomes, identification of disease mechanisms linking COPD to MetS is the key to effective therapy. In this comprehensive review, we discuss the potential mechanisms linking MetS to COPD and hence plausible therapeutic strategies to treat this debilitating comorbidity of COPD.